Fault detection and diagnosis are an important aspect of condition-based maintenance systems, which collect the signatures of operable machines utilizing multiple sensors for subsequent analysis. Rendering devices such as printers, for example, may include defects that are detected after a product has been launched. Image quality related faults, for example, are a major contributor to post sale service run costs and therefore a need exists to quickly initiate repair actions with respect to such faults.
The majority of prior art diagnostic approaches permit the rendering device to request service automatically when the sensors detect particular operating parameters outside permissible ranges. One problem with such diagnostic techniques is that they are static and incapable of adapting to changes associated with the rendering characteristics. Also, such techniques do not respond to the various number of image quality related faults that are often detected by service engineers after a product is launched. Such diagnostic method and systems do not provide for any formal mechanism that determines when to re-train an underlying diagnoser and thus can lead to customer dissatisfaction. Hence, it is important to incorporate new knowledge and update the diagnosers as new defects are discovered. Additionally, training a new classifier entails time and effort, so performing unnecessary training is uneconomical, whereas a lack of training in the presence of new faults will reduce the efficacy and accuracy of the classifier.
Based on the foregoing, it is believed that a need exists for an improved method and system for updating a fault diagnoser based on online monitoring of the performance of a classifier. A need also exists for an improved method for automatically diagnosing new rendering device faults, as described in greater detail herein.